Apache Orc VS Apache Thrift

The information can be stored in a database or as files, serialized in a standard format and with a schema agreed with your Data Engineering team. Depending on your information and requirements, it can be as simple as CSV, XML or JSON, or Big Data formats such as Parquet, Avro, ORC, Arrow, or message serialization formats like Protocol Buffers, FlatBuffers, MessagePack, Thrift, or Cap'n Proto.

Data formatting is another place to make gains. When dealing with huge amounts of data, finding the data you need can take up a significant amount of your compute time. Apache Parquet and Apache ORC are columnar data formats optimized for analytics that pre-aggregate metadata about columns. If your EMR queries column intensive data like sum, max, or count, you can see significant speed improvements by reformatting data like CSVs into one of these columnar formats.

The following stack captures layers of software components that make up Hudi, with each layer depending on and drawing strength from the layer below. Typically, data lake users write data out once using an open file format like Apache Parquet/ORC stored on top of extremely scalable cloud storage or distributed file systems. Hudi provides a self-managing data plane to ingest, transform and manage this data, in a way that unlocks incremental data processing on them.

There are various notable implementations of RPC like Apache Thrift and gRPC.

> That's just your choice of how to build your app, right? You could've avoided this by rendering templates on the server and sending static HTML to the client, keeping the business logic on the server.

No, that's a requirement on most business cases, my comment stated 'complex and dynamic web apps'. Re-rendering the whole page everytime the user checks a box or clicks a button is (a) terrible UX, (b) hard to track the state between page refresh, (c) wrong practice and (d) bad performance.

> Here's just one of ten-thousand other battle-tested options you can use: https://github.com/apache/thrift/

Sure, I should setup a complex and huge dependency for just one of the many problems I highlighted. What a great idea

Thus, we released Baseplate.py v2.1 with deadline propagation. Each request between Baseplate services has an associated THeader, which includes relevant information for Baseplate to fulfill its functionality, such as tracing request timings. We added a “Deadline-Budget” field to this header that propagates the remaining timeout so that information is available to the following request, and this timeout continues to get updated with every new request made. With this update, we save production costs by allowing resources to work on requests awaiting a response, and gain overall improved latency.

Here’s an example of the Thrift changelog. Knock yourself out. Or you can get your sense of productivity by actually doing something of value.

The biggest addition is native async reading via futures::AsyncRead and futures::AsyncSeek, which required a lot of (to be merged) changes upstream (changes to thrift rust compiler and parquet-format-rs). I placed those changes on a temporary crate until things are released there.

We were sharing instances of a Thrift TDeserializer across threads. We knew TProtocol was not thread-safe, but the TDeserializer constructor accepts a TProtocolFactory, so we naively assumed the deserialize method would use that to create a new instance of TProtocol for each invocation, but unfortunately, the TDeserializer constructor immediately creates TProtocol and stores it in a member variable, so TDeserializer is not actually thread-safe.